Jev AI Model Targets Structured Decisions Over Text Generation

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Jev is a new AI model developed by TypeSafe that is designed to output structured decisions with confidence scores rather than generating natural language text. Instead of producing paragraphs, it takes a defined state and a set of questions, then returns typed answers such as yes/no classifications, category routes, and severity levels alongside probability scores. The model uses a training approach called Reinforcement Learning for Calibrated Decisions (RLCD), which aims to make its confidence scores statistically meaningful rather than self-reported estimates. TypeSafe has published pricing of approximately $0.042 per million input tokens with output tokens free, which the company estimates could amount to around $1.26 per 100,000 triage-style decisions. The architecture is positioned for high-volume use cases such as support-ticket routing, content moderation, agent orchestration, and document classification, where speed and cost per decision matter more than verbose output.

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